ZHe data
Data from Pikes Peak, Mt. Evans, Longs Peak
Rachel Havranek
25 Apr 2020
Pikes Peak
Load and Manipulate Data
Pikes_Data <- read.csv("data/Pikes_Data_for_R .csv")
AFT_data <- read.csv("data/AFT_Data.csv")
#Modify the sample names, for ease of reading, where PP still stands for Pikes Peak, and they are numbered 1 - 6 with the lowest elevation sample being 1 and highest elevation sample being 6
Pikes_Data <- Pikes_Data %>%
mutate (papername =
ifelse(Sample_Name == "PP2084", "PP1", #If True, add label PP2
ifelse( Sample_Name == "PP2479", "PP2", #If True, add label PP2
ifelse (Sample_Name == "PP2907", "PP3", #If true, add label PP3
ifelse (Sample_Name == "PP3597", "PP4", #If true, add label PP4
ifelse (Sample_Name == "PP3971", "PP5", "PP6") #if true, add label PP5, ELSE add the label PP6
)
)
)
)
)Plots
This section makes plots for:
1. Pikes Peak Elevation - date
2. Pikes Peak Date-eU
3. Pikes Peak Elevation vs. date with each point colored by eU
4. Pikes Peak date vs. grain size
3. Pikes Peak AFT data (Kelley and Chapin, 2004)
## [1] "Excludes bottom two AFT samples at 1777m and 1866m"## Warning: Removed 2 rows containing missing values (geom_point).
Hefty Inputs
Grains excluded from models:
* Sample: PP1, grain:Z32, rownumber: 6 - this grain has an eU of 151.5 and a date of 312.9, which is ~ 300 Ma younger than grains w/ comprable eU
** I have divided by data into 5 bins with roughly equal number of grains:** * 0 - 150 ppm (7 grains) * 150 - 350 ppm (6 grains) * 350 - 500 ppm (6 grains) * 500 - 900 ppm (7 grains) * 900 - 2000 ppm (5 grains)
I have chosen to model all of the samples together because they form a singular date-eU trend. I like the bins I have chosen here becuase I think they accurately capture the overall date - eU span. - 670.04, eU = 102.46
- 627.27, eU = 250.52
- 288.52, eU = 447.82
- 264.16, eU = 676.41
- 140.76, eU = 1323.12
One potential way to change this would be to take out the second bin (so there are 4 bins total), and make that bin slightly larger (i.e. 0 - 200, and then make the second bin 200 to 500). I didn’t choose that approach here becuase that would make the first two bins have 9 and 10 grains, respecively, while the next two bins would have 7 and 5. (But maybe that’s not so bad). - 676.93, eU = 120.09
- 469.26, eU = 382.64 -> this is an intermediate between bins 2 and 3 above, all in all I don’t think it would alter the model that much because its errorbars are so big - 264.16, eU = 676.41
- 140.76, eU = 1323.12
grains.not.modeled <- c(6)
Pikes_Data <- Pikes_Data %>%
mutate(
Rownumber= row_number(),
Donotuse = (Rownumber %in% grains.not.modeled),
bindata= cut(eU, c(0,150, 350,500,900,2500)) #these are my bin cutoffs
) Here I calculate the parapmeters I will need for my hefty model input. This automatically saves to a CSV in my data_output folder, and can be easily accessed and shared
## # A tibble: 5 x 15
## bindata N RawDate_mean Rawdate_15perce… Rawdate_SD CorrDate_mean
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 (0,150] 7 546. 82.0 30.0 670.
## 2 (150,3… 6 519. 77.8 67.1 627.
## 3 (350,5… 6 295. 44.2 88.0 389.
## 4 (500,9… 7 208. 31.2 86.8 264.
## 5 (900,2… 5 120 18 36.7 141.
## # … with 9 more variables: CorrDate_15percent <dbl>, CorrDate_SD <dbl>,
## # mean_rs <dbl>, U <dbl>, Th <dbl>, Sm <dbl>, eU <dbl>, He <dbl>, FT <dbl>## [1] "This is a dynamic plot that can be zoomed in on, and if you hover over a data point, you can see what grain it is"
Mount Evans
Load and Manipulate Data
#Read in data (same format, with modified headers, as what will be published)
Mount_Evans <- read.csv("data/Evans Data.csv")
#Add a column with the sample names from original collection.
Mount_Evans <- Mount_Evans %>% mutate(
collection.name = ifelse (Sample == "ME1_2872", "ME10-16",
ifelse (Sample == "ME2_3596", "ME8-16",
ifelse (Sample == "ME3_3978", "ME3-16",
"ME1-16")
)
)
)
#Add in a column with sample elevation
Mount_Evans <- Mount_Evans %>% mutate(
Elevation = ifelse (Sample == "ME1_2872", 2872,
ifelse (Sample == "ME2_3596", 3596,
ifelse (Sample == "ME3_3978", 3978,
4345)
)
)
)Plots
## [1] "Excludes the two highest eU data points"
Hefty Outputs
For this sample I’ve chosen to use differnt bins than the Pikes Peak samples - but I’ve used similar, but slightly different guiding principles. In particular I tried to ‘lump’ groups of grains together, so that my averages center in what look like distinct populations.
Bins:
* 0 - 400 (9 grains)
* 400 - 700 (8 grains)
* 700 - 1600 (5 grains)
* 1600 - 5100 (2 grains)
grains.not.modeled <- c()
Mount_Evans <- Mount_Evans %>%
mutate(
Rownumber= row_number(),
Donotuse = (Rownumber %in% grains.not.modeled),
bindata= cut(eU, c(0,400, 700,1600,5100)) #these are my bin cutoffs
) Here I calculate the parapmeters I will need for my hefty model input. This automatically saves to a CSV in my data_output folder, and can be easily accessed and shared
## # A tibble: 4 x 15
## bindata N RawDate_mean Rawdate_15perce… Rawdate_SD CorrDate_mean
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 (0,400] 9 401. 60.2 58.8 486.
## 2 (400,7… 8 253. 38.0 74.9 308.
## 3 (700,1… 5 162. 24.3 57.6 192.
## 4 (1.6e+… 2 34.8 5.22 8.06 39.8
## # … with 9 more variables: CorrDate_15percent <dbl>, CorrDate_SD <dbl>,
## # mean_rs <dbl>, U <dbl>, Th <dbl>, Sm <dbl>, eU <dbl>, He <dbl>, FT <dbl>## [1] "This is a dynamic plot that can be zoomed in on, and if you hover over a data point, you can see what grain it is"
Longs Peak
Load and Manipulate Data
Longs_Peak <- read.csv("data/Longs_Peak.csv")
#Rename sample to match other peak schema
Longs_Peak <- Longs_Peak %>% mutate (
papername = ifelse (Sample == "LP1", "LP7",
ifelse (Sample == "LP2", "LP6",
ifelse (Sample == "LP3", "LP5",
ifelse (Sample == "LP4", "LP4",
ifelse (Sample == "LP7", "LP3",
ifelse(Sample == "LP5", "LP2", "LP1")
)
)
)
)
)
)
# Add in column with sample elevation
Longs_Peak <- Longs_Peak %>% mutate (
Elevation = ifelse (Sample == "LP1", 4343,
ifelse (Sample == "LP2", 4121,
ifelse (Sample == "LP3", 4023,
ifelse (Sample == "LP4", 3688,
ifelse (Sample == "LP7", 3500,
ifelse(Sample == "LP5", 3383, 2835)
)
)
)
)
)
) Plots
Hefty Outputs
Here, I’ve chosen to exclude the grain from LP 1 with a date of 137 ma, beucase it is an outlier from the rest of the data ###### Option #1 This grouping takes into account the ‘populations’ approach, it groups grains based on eU into what visually look like clusters 4 Bins:
0 - 500 (4 grains)
500 - 900 (2 grains)
900 - 1600 (7 grains)
1600 - 2400 (2 grains)
Option #2:
This grouping is tempting becuase it takes the first three bins of Pikes Peak, collapses them into 1, and then uses the same boundaries as bins 4 and 5 3 Bins: 0 - 500 (4 grains) 500 - 9000 (2 grains)
*900 - 2000 (9 grains)
Option #3:
Even eU bins - I think this makes the most sense in some ways for this distribution, because it honors ‘clusters’ for the most part, and evenly divides the eU space. Downside: it is not particularly similar to either of the groupings from the other peaks 4 Bins: 0 - 500 (4 grains) 500 - 1000 (3 grains)
1000 - 1500 (6 grains) 1500 - 2000 (2 grains)
None of these options really meaningfully change the average dates or the uncertainties (either 15% or s.d.) in each resepctive bin.
grains.not.modeled <- c(1)
Longs_Peak <- Longs_Peak %>%
mutate(
Rownumber= row_number(),
Donotuse = (Rownumber %in% grains.not.modeled),
bindata= cut(eU, c(0,500, 1000, 1500, 2200)) #these are my bin cutoffs
) Here I calculate the parapmeters I will need for my hefty model input. This automatically saves to a CSV in my data_output folder, and can be easily accessed and shared
## # A tibble: 4 x 14
## bindata N RawDate_mean Rawdate_15perce… Rawdate_SD CorrDate_mean
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 (0,500] 4 42.9 6.44 7.98 57.6
## 2 (500,1… 3 47.8 7.17 6.69 62.7
## 3 (1e+03… 6 36.9 5.54 9.67 57.1
## 4 (1.5e+… 2 34.0 5.09 2.05 49.6
## # … with 8 more variables: CorrDate_15percent <dbl>, CorrDate_SD <dbl>,
## # mean_rs <dbl>, U <dbl>, Th <dbl>, eU <dbl>, He <dbl>, FT <dbl>## [1] "This is a dynamic plot that can be zoomed in on, and if you hover over a data point, you can see what grain it is"